This dataset provides a realistic simulation of loan applications, containing 5000 entries and 15 features that reflect key factors influencing loan approvals. It is designed for exploratory data analysis (EDA), feature engineering, and machine learning modeling.

The dataset does not include the loan approval decision, allowing users to build their own prediction models.

This dataset provides information about people applying for loans, including details on their personal background, finances, and loan specifics. It's meant to help us better understand how different personal factors impact whether a loan gets approved. The data includes things like the applicant's age, income, home ownership status, job history, and credit score, along with loan details such as the loan amount, interest rate, and purpose. It also shows whether the loan was approved or denied.

Features in the dataset:

1. person_age: The applicant's age.
2. person_income: How much the applicant earns annually, in USD.
3. person_home_ownership: Whether the person owns, rents, or has a mortgage on their home.
4. person_emp_length: How long the person has been employed.
5. loan_intent: The reason for applying for the loan (e.g., EDUCATION, MEDICAL, VENTURE, DEBT CONSOLIDATION, PERSONAL).
6. loan_grade: The credit grade given to the loan (A, B, C, D, etc.).
7. loan_amnt: The amount of money the applicant is requesting for the loan, in USD.
8. loan_int_rate: The interest rate on the loan.
9. loan_percent_income: The percentage of the applicant’s income being requested as a loan.
10. cb_person_default_on_file: Whether the applicant has defaulted on any previous loans (Y/N).
11. cb_person_cred_hist_length: The length of the applicant's credit history, in years.
12. loan_status: The outcome of the loan application (0 = Rejected, 1 = Accepted).

This dataset provides insights into loan applicants' characteristics and their risk assessment. It comprises information on various attributes of loan applicants, including demographic details, financial status, employment history, and ownership status. The dataset includes both numerical and categorical features, making it suitable for diverse analytical approaches.

Key Features:

1. Id: Unique identifier for each loan applicant.
2. Income: The income level of the applicant.
3. Age: Age of the applicant.
4. Experience: Years of professional experience.
5. Married/Single: Marital status of the applicant.
6. House_Ownership: Indicates whether the applicant owns or rents a house.
7. Car_Ownership: Indicates whether the applicant owns a car.
8. Profession: Occupation or profession of the applicant.
9. CITY: City of residence of the applicant.
10. STATE: State of residence of the applicant.
11. CURRENT_JOB_YRS: Duration of employment in the current job.
12. CURRENT_HOUSE_YRS: Duration of residence in the current house.
13. Risk_Flag: Binary indicator of loan risk, where 1 represents a flagged risky applicant and 0 represents a non-risky applicant.

This dataset contains 252,000 entries and provides a comprehensive overview of loan applicants' profiles, enabling analysis and modeling for risk assessment and decision-making in lending processes.

Dataset

This dataset was created by Yogeshwari2110

Released under Apache 2.0

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This dataset was created by hydracsnova

Released under MIT

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This dataset was created by Huy Nguyen

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This dataset was created by Akash Selvadoss N

Released under Apache 2.0

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This dataset was created by Ziad Amer

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This dataset was created by Lakshay Handa

Released under Apache 2.0

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This dataset was created by EK_Wunu

Released under CC0: Public Domain

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This dataset was created by Prem ranjan Pattanayak

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This dataset was created by Shrishti Tayde

Released under MIT

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This dataset was created by Armanjit Singh

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Dataset Card: Loan Prediction Dataset

Overview The Loan Prediction Dataset is designed for binary classification tasks where the goal is to predict whether a loan should be approved or denied based on applicant information. This dataset includes demographic and financial features commonly used by financial institutions to assess loan eligibility.

Dataset Contents The dataset consists of 1,000 samples, each representing an individual loan application with the following features:

• age (integer): Age of the applicant in years, ranging from 20 to 70.
• income (float): Annual income of the applicant in dollars, ranging from approximately 4,000 to 100,000, with a right.
• skewed distribution (more values in the lower range).
• credit_score (integer): Credit score of the applicant, ranging from 300 to 850.
• dependents (integer): Number of dependents the applicant has, ranging from 0 to 4.
• home_owner (binary, 0/1): Indicates if the applicant owns a home (1 for yes, 0 for no).
• loan_approved (binary, 0/1): Target variable (1 for approved, 0 for denied), indicating the loan approval decision. Purpose This dataset can be used for building and evaluating machine learning models for loan approval prediction. It is suitable for various classification algorithms (e.g., logistic regression, decision trees, random forests) and is intended to help users explore feature importance, model interpretability, and performance metrics for binary classification tasks in finance-related applications.

Dataset

This dataset was created by Abdul Raheem

Released under CC BY-NC-SA 4.0

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This dataset was created by Akshay Kumar

Released under Apache 2.0

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This dataset was created by Mahdi Ravaghi

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This dataset was created by Ahamed Safnas

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Dataset

This dataset was created by Nguyễn Quốc Gia Huy

Released under Apache 2.0

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